TIEM: Temporal Integration of Hypergraph Evidence and Skill Memory for Event-Driven Financial Forecasting

2026-08-13

AI Summary

This work addresses the “evidence gap” in event-driven financial forecasting caused by training data contamination and temporal leakage. To tackle this issue, the authors propose TIEM, a timestamp-gated framework that constructs an Event-Evidence Hypergraph (EEH) for multi-level temporal retrieval, incorporates a Case-based Skill Memory (CSM) module with source-aware labels to store temporal reasoning skills, and employs a Heterogeneous Evidence-Experience Fusion Reasoning (HEFR) mechanism for prediction. To rigorously evaluate a model’s genuine temporal sensitivity, they introduce the FinPURE benchmark along with the Name-Date Probe method. Experimental results demonstrate that TIEM significantly outperforms existing approaches across five financial forecasting benchmarks, confirming its effectiveness in realistic temporal settings.

Abstract

Event-driven catalyst-outcome forecasting increasingly uses retrieval- and memory-augmented large language model agents for prediction. However, training-data contamination and temporal leakage can create an Evidence Chasm between reported accuracy and true predictive ability. We propose TIEM, a timestamp-gated framework with three coordinated components: an Event-Evidence Hypergraph (EEH) for timestamp-filtered multi-tier retrieval; a Case-based Skill Memory (CSM) for source-tagged temporal skills; and Heterogeneous Evidence-Experience Fusion Reasoning (HEFR) for evidence-experience fusion and prediction. We also introduce FinPURE, a recent-period A-share holdout benchmark, and use a Name-Date Probe to assess per-model name-date sensitivity rather than assuming training cutoffs. Results on five financial forecasting benchmarks show TIEM outperforms current baselines. Our project is available at https://github.com/QwenQKing/Fin_TIEM.

Problem

• Evidence Chasm
• Temporal Leakage
• Training-data Contamination
• Event-driven Forecasting
• Financial Prediction

Innovation

• Temporal Integration
• Hypergraph Evidence
• Skill Memory
• Evidence-Experience Fusion
• Event-Driven Forecasting